Shane, One issue that make that version of the paper controversial is the term "computation", which actually has two senses: (1) "whatever computer does",and (2) "what defined as `computation' in computability theory". In the paper I'm using the second sense of the term. (I'm revising the paper to make this more clear.)
My argument, briefly speaking, is that it is quite possible, in the current computer, to solve problems in such a way that is non-deterministic (i.e., context-sensitive) and open-ended (as in anytime algorithms). Such a process doesn't satisfy the definition of computation, doesn't follow a predetermined algorithm, and has no fixed complexity. To implement such a process requires no magic --- actually many existing systems already go beyond computability theory, though few people has realized it. An concrete example is my NARS --- there is a demo at http://www.cogsci.indiana.edu/farg/peiwang/NARS/ReadMe.html (you know that, but some others don't). The system's capacity at the surface level cannot be specified by computability theory, and the resource it spends on a question is not fixed. For that "level" issue, one way to see it is through the concept of "virtual machine". We all know that at a low level computer only has procedural language and binary data, but at a high level it has non-procedural language (such as functional or logical languages) and decimal data. Therefore, if virtual machine M1 is implemented by virtual machine M2, the two may still have quite different properties. What I'm trying to do is to implement a "non-computing" system on a computing one. If you are still unconvinced, think about this problem: say the problem you are trying to solve is to reply my current email. Is this problem computable? Do you follow an algorithm in solving it? What is the computational complexity of this process? Pei ----- Original Message ----- From: "Shane Legg" <[EMAIL PROTECTED]> To: <[EMAIL PROTECTED]> Sent: Saturday, January 11, 2003 5:12 PM Subject: Re: [agi] AI and computation (was: The Next Wave) > Pei Wang wrote: > > In my opinion, one of the most common mistakes made by people is to think AI > > in terms of computability and computational complexity, using concepts like > > Turing machine, algorithm, and so on. For a long argument, see > > http://www.cis.temple.edu/~pwang/551-PT/Lecture/Computation.pdf. Comments > > are welcome. > > It's difficult for me to attack a specific point after reading > through your paper because I find myself at odds with your views > in many places. My views seem to be a lot more orthodox I suppose. > > Perhaps where our difference is best highlighted is in the > following quote that you use: > > “something can be computational at one level, > but not at another level” [Hofstadter, 1985] > > To this I would say: "Something can LOOK like computation > at one level, but not LOOK at computation at another level. > Nevertheless it still is computation and any limits due to > the fundamental properties of computation theory still apply." > > Or to use an example from another field: A great painting > involves a lot more than just knowledge of the physical > properties of paint. Nevertheless, a good painter will know > the physical properties of his paints well because he knows > that the product of his work is ultimately constrained by these. > > That's one half of the story anyway; the other part is that I > believe that intelligence is definable at a pretty fundamental > level (i.e. not much higher than the concept of universal Turing > computation) but I'll leave that part for now and focus on this > first issue. > > Shane > > ------- > To unsubscribe, change your address, or temporarily deactivate your subscription, > please go to http://v2.listbox.com/member/?[EMAIL PROTECTED] > ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
